Reinforcement learning used to be genuinely tough—evaluating agent actions, determining proper rewards and penalties, attributing outcomes to specific components. It was messy.

That's shifted dramatically. Large language models now handle the heavy lifting on evaluation tasks. With LLMs managing assessment and feedback loops, what once required painstaking manual design became algorithmically feasible. The bottleneck broke open.